Development and Application of Alternative Learning Methodologies for Predictive Learning

Proposal Number: ECCS-0802056

Proposal Title: Development and Application of Alternative Learning Methodologies for Predictive Learning

PI Name: Cherkassky, Vladimir S.

PI Institution: University of Minnesota-Twin Cities

The objective of this research is investigation of emerging technologies for estimating predictive models from sparse heterogeneous data. Such problems are common in biomedical applications, such as micro-array data analysis, and structural & functional brain imaging. In medical applications, diagnostic (predictive) models are usually estimated from heterogeneous data. For example, for cancer diagnosis, patients? data may include clinical, demographic and genomic inputs. The proposed approach emphasizes direct formulation of the learning problem that takes full advantage of application-domain requirements and known characteristics of application data. Proposed work will investigate several novel learning formulations including Learning with Structured Data and closely related Multi-Task Learning, and a new method for incorporating a priori knowledge into the learning process called Learning through Contradictions. These non-standard learning approaches can potentially improve classification (diagnostic) accuracy for many biomedical applications.

Intellectual merit is the development and improved understanding of several alternative learning settings, which show great promise for predictive modeling with sparse heterogeneous data. Relative advantages and limitations of these new approaches (vs. standard inductive learning) will be investigated for several biomedical applications.

Broader impact include:

-Improved diagnosis for biomedical applications that utilize heterogeneous data (i.e., clinical, genetic and demographic information).

-Methodological impact of the growing importance of alternative learning formulations on data mining applications.

-Incorporating new (non-standard) learning methodologies into graduate and undergraduate curriculum.